Predicting prices of S&P500 index using classical methods and recurrent neural networks

Mateusz Kijewski and Robert Ślepaczuk
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Mateusz Kijewski: Quantitative Finance Research Group; Faculty of Economic Sciences, University of Warsaw

No 2020-27, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: This study implements algorithmic investment strategies with buy/sell signals based on classical methods and recurrent neural network model (LSTM). The research compares the performance of investment algorithms on time series of S&P500 index covering 20 years of data from 2000 to 2020. This paper presents an approach for dynamic optimization of parameters during backtesting process by using rolling training-testing window. Every method was tested in terms of robustness to changes in parameters and evaluated by appropriate performance statistics e.g. Information Ratio, Maximum Drawdown, etc. Combination of signals from different methods was stable and outperformed benchmark of Buy & Hold strategy doubling its returns on the same level of risk. Detailed sensitivity analysis revealed that classical methods which used rolling training-testing window were significantly more robust to changes in parameters than LSTM model in which hyperparameters were selected heuristically.

Keywords: machine learning; recurrent neural networks; long short-term memory model; time series analysis; algorithmic investment strategies; systematic transactional systems; technical analysis; ARIMA model (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 52 pages
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-ore
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2020-27

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